Abstract
To develop a realistic simulator for autonomous vehicle testing, the simulation of various scenarios that may occur near vehicles in the real world is necessary. In this paper, we propose a new scenario generation pipeline focused on generating scenarios in a specific area near an autonomous vehicle. In this method, a scenario map is generated to define the scenario simulation area. A convolutional neural network (CNN)-based scenario agent selector is introduced to evaluate whether the selected agents can generate a realistic scenario, and a collision event detector handles the collision message to trigger an accident event. The proposed event-centric action dispatcher in the pipeline enables agents near events to perform related actions when the events occur near the autonomous vehicle. The proposed scenario generation pipeline can generate scenarios containing pedestrians, animals, and vehicles, and, advantageously, no user intervention is required during the simulation. In addition, a virtual environment for autonomous driving is also implemented to test the proposed scenario generation pipeline. The results show that the CNN-based scenario agent selector chose agents that provided realistic scenarios with 92.67% accuracy, and the event-centric action dispatcher generated a visually realistic scenario by letting the agents surrounding the event generate related actions.
Highlights
Autonomous driving has been a hot research topic since the end of the last century [1] because it promises many benefits, such as increased safety, reduced traffic congestion, and time savings
To verify the proposed scenario generation pipeline, we show the results of the scenario map generator, the convolutional neural network (CNN)-based scenario agent selector, and the event-centric action dispatcher
Experimental results we show the results of the scenario map generator, CNN-based scenario agent selector, and event-centric action dispatcher
Summary
Autonomous driving has been a hot research topic since the end of the last century [1] because it promises many benefits, such as increased safety, reduced traffic congestion, and time savings. The first thing to consider when developing human-centric autonomous vehicles is safety [2]. The development of autonomous vehicles in the real world faces many problems, such as bad weather and difficulties in data collection. In the past few decades, a variety of simulators have been developed for various purposes in machine learning [3], such as training employees [4, 5], soldiers [6], collecting training datasets [7], training models, and testing algorithms [8,9,10,11]. To build a simulator for autonomous vehicles equipped with various kinds of sensors, in addition to realistic visual effects [11], realistic simulation of possible real-world scenarios is essential. A simulator should model various scenarios encountered by a vehicle in the real world.
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